Patterns were learned on the 6mo MENs and the gut cell atlas was projected into them. Now look at the subset of non-fetal healthy samples and visualize the pattern usages
library(monocle3)
library(dplyr)
library(here)
source(here("scripts/accessory_functions/monocle_mods.R"))
source(here("scripts/accessory_functions/pattern_plotting.R"))
obj_fn <- "/data/users/jared/atlas_processing/gut_cell_atlas/data/mesenchymal_gut_nonfetal_healthy.Rds"
cds <- readRDS(obj_fn)
Pattern definitions in 6month LMMP
knitr::include_graphics(here("plots/NMF/lmmp/old_pattern_run/50dims/LMMP_pattern_feature_correlation.pdf"))
cell_wt <- data.table::fread(here("teichman_gut_atlas/data/gut_atlas_in_6mo_LMMP_old_patterns.csv"), data.table = F)
colnames(cell_wt)[1] <- "cell_index"
all(colnames(cds) %in% rownames(cell_wt))
## [1] FALSE
From previously generated pattern projections, load in values and merge with existing pData
pData(cds) <- pData(cds) %>%
as.data.frame() %>%
tibble::rownames_to_column(var = "cell_index") %>%
left_join(., cell_wt, by = "cell_index") %>%
tibble::column_to_rownames(var = "cell_index") %>%
DataFrame
plot_cells_mod(cds, color_cells_by = "Age") + facet_wrap(~ Age)
## No trajectory to plot. Has learn_graph() been called yet?
## Warning: The `add` argument of `group_by()` is deprecated as of dplyr 1.0.0.
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## `summarise()` has grouped output by 'cell_group'. You can override using the `.groups` argument.
plot_cells_mod(cds, color_cells_by = "Integrated_05", label_cell_groups = F) +
theme(legend.position = "none") +
facet_wrap(~ Integrated_05)
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npatt <- 50
lapply(1:npatt, plotCellPatterns, cds, do.clip = c(0.01,0.99))
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saveRDS(cds, here("teichman_gut_atlas/data/mesenchymal_gut_nfh_annotated.rds"))